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A search of MERLOT materialsCopyright 1997-2015 MERLOT. All rights reserved.Sun, 2 Aug 2015 13:47:47 PDTSun, 2 Aug 2015 13:47:47 PDTMERLOT Search - category=2636&materialType=Online%20Course&sort.property=overallRatinghttp://www.merlot.org:80/merlot/images/merlot.gifhttp://www.merlot.org:80/merlot/
44346.01SC Introduction to Electrical Engineering and Computer Science I (MIT)http://www.merlot.org/merlot/viewMaterial.htm?id=884421
This course provides an integrated introduction to electrical engineering and computer science, taught using substantial laboratory experiments with mobile robots. Our primary goal is for you to learn to appreciate and use the fundamental design principles of modularity and abstraction in a variety of contexts from electrical engineering and computer science. Our second goal is to show you that making mathematical models of real systems can help in the design and analysis of those systems. Finally, we have the more typical goals of teaching exciting and important basic material from electrical engineering and computer science, including modern software engineering, linear systems analysis, electronic circuits, and decision-making.Wed, 6 Aug 2014 17:11:51 -0700Artificial Intelligence: Introduction to Roboticshttp://www.merlot.org/merlot/viewMaterial.htm?id=351548
Introduction to Robotics is one of the ten free courses being offered to the public through Stanford Engineering Everywhere. The course belongs to the Artificial Intelligence series and is taught by Professor Oussama Khatib of Stanford University's Computer Science Department. The purpose of this course is to introduce you to basics of modeling, design, planning, and control of robot systems. In essence, the material treated in this course is a brief survey of relevant results from geometry, kinematics, statics, dynamics, and control.The course is presented in a standard format of lectures, readings and problem sets. Topics: robotics foundations in kinematics, dynamics, control, motion planning, trajectory generation, programming and design.Thu, 20 Nov 2008 10:20:30 -0800Artificial Intelligence: Machine Learninghttp://www.merlot.org/merlot/viewMaterial.htm?id=351579
Machine Learning is one of the ten free courses being offered to the public through Stanford Engineering Everywhere. The course belongs to the Artificial Intelligence series and is taught by Andrew Ng, Assistant Professor of Stanford University&#39;s Computer Science Department. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.Thu, 20 Nov 2008 13:59:28 -0800Courseware : Soft Computinghttp://www.merlot.org/merlot/viewMaterial.htm?id=447575
Soft Computing : Course lectures, hours 42. There are 9 pdf files, total 398 pages. Topics : Introduction to Soft Computing; Fundamentals of Neural Network; Back Propagation Network; Associative Memory; Adaptive Resonance Theory; Fuzzy Set Theory; Fuzzy Systems; Fundamentals of Genetic Algorithms; Hybrid Systems.Tue, 27 Apr 2010 02:09:00 -070015.097 Prediction: Machine Learning and Statistics (MIT)http://www.merlot.org/merlot/viewMaterial.htm?id=884568
Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the &quot;information overload&quot; that characterizes our current digital age. Machine learning developed from the artificial intelligence community, mainly within the last 30 years, at the same time that statistics has made major advances due to the availability of modern computing. However, parts of these two fields aim at the same goal, that is, of prediction from data. This course provides a selection of the most important topics from both of these subjects.Wed, 6 Aug 2014 17:14:44 -070024.09 Minds and Machines (MIT)http://www.merlot.org/merlot/viewMaterial.htm?id=883846
This course is an introduction to many of the central issues in a branch of philosophy called philosophy of mind.Wed, 6 Aug 2014 16:59:58 -07006.868J The Society of Mind (MIT)http://www.merlot.org/merlot/viewMaterial.htm?id=884024
This course is an introduction to the theory that tries to explain how minds are made from collections of simpler processes. It treats such aspects of thinking as vision, language, learning, reasoning, memory, consciousness, ideals, emotions, and personality. It incorporates ideas from psychology, artificial intelligence, and computer science to resolve theoretical issues such as wholes vs. parts, structural vs. functional descriptions, declarative vs. procedural representations, symbolic vs. connectionist models, and logical vs. common-sense theories of learning.Wed, 6 Aug 2014 17:03:41 -07009.66J / 9.660J / 6.804J Computational Cognitive Sciencehttp://www.merlot.org/merlot/viewMaterial.htm?id=591584
This course is an introduction to computational theories of human cognition. Drawing on formal models from classic and contemporary artificial intelligence, students will explore fundamental issues in human knowledge representation, inductive learning and reasoning. What are the forms that our knowledge of the world takes? What are the inductive principles that allow us to acquire new knowledge from the interaction of prior knowledge with observed data? What kinds of data must be available to human learners, and what kinds of innate knowledge (if any) must they have?Thu, 20 Oct 2011 13:57:42 -07009.913 Pattern Recognition for Machine Visionhttp://www.merlot.org/merlot/viewMaterial.htm?id=591712
The applications of pattern recognition techniques to problems of machine vision is the main focus for this course. Topics covered include, an overview of problems of machine vision and pattern classification, image formation and processing, feature extraction from images, biological object recognition, bayesian decision theory, and clustering.Thu, 20 Oct 2011 13:57:55 -0700Advanced Artificial Intelligencehttp://www.merlot.org/merlot/viewMaterial.htm?id=620084
This course will present advanced topics in Artificial Intelligence (AI), including inquiries into logic, artificial neural network and machine learning, and the Turing machine. This free course may be completed online at any time. See course site for detailed overview and learning outcomes. (Computer Science 408)Fri, 27 Jan 2012 10:50:33 -0800